class_docstr: The method class for G-SphereNet algorithm proposed in the paper `An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch <https://openreview.net/forum?id=C03Ajc-NS5W>`_. 
  This class provides interfaces for running training and generation with G-SphereNet algorithm. 
  Please refer to the `example codes <https://github.com/divelab/DIG/tree/dig-stable/examples/ggraph3D/G_SphereNet>`_ for usage examples.
code: def get_model(self, model_conf_dict, checkpoint_path=None):
      if model_conf_dict["use_gpu"] and not torch.cuda.is_available():
          model_conf_dict["use_gpu"] = False
      self.model = SphGen(**model_conf_dict)
      if checkpoint_path is not None:
          self.model.load_state_dict(torch.load(checkpoint_path))
  
file_path: DIG/dig/ggraph3D/method/G_SphereNet/gspherenet.py
imports: import os
  import torch
  import numpy
  from .model import SphGen
label_desc: Use image node_id 2 for calling the G_SphereNet obj's underlying member method code with example usage: obj.get_model(model_conf_dict, checkpoint_path) without return types